A dataset of observations may be captured in a matrix as a function of possibly correlated parameters. Each parameter represents a definable characteristic of a subject captured in the dataset. In some cases, the dataset can be segmented into a smaller set of parameters while maintaining as much of the variance in the dataset as possible. In cluster analysis, the ability to maximize a distance between distinct clusters of the dataset, also known as space dilation, is important to yield useful results from the segmented dataset. Identification and selection of an appropriate set of parameters representative of the dataset is important to increase the space dilation. For example, to segment customers based on p attributes using cluster analysis, there is separation between the clusters or like groupings of customers based on the p attributes. Increasing the distance between the clusters defines clusters more distinctly, which results in improved associations/disassociations between the customers (subjects). Selecting too many variables can decrease the space dilation because extreme values blend with less extreme values per observation.